Network Analysis of Sport-related Concussion Research During the Past Decade (2010–2019)
Bibliographic record
Abstract
CONTEXT: There has been substantial growth over the past decade in sport-related concussion (SRC) research, yet no research to date has synthesized developments over this critical time period. OBJECTIVE: to apply a network analysis approach to evaluate trends in the sport-related concussion (SRC) literature using a comprehensive search of original, peer-reviewed research articles involving human participants published between January 1, 2010 and December 31, 2019. DESIGN: Narrative review. MAIN OUTCOME MEASURES: Bibliometric maps were derived from a comprehensive search of all published, peer-reviewed SRC articles on the Web of Science database. A clustering algorithm was used to evaluate associations among journals, organizations/institutions, authors, and keywords. The online search yielded 6,130 articles, 528 journals, 7,598 authors, 1,966 organizations, and 3,293 keywords. RESULTS: The analysis supported five thematic clusters of journals: 1. Biomechanics/Sports medicine (n=15), 2. Pediatrics/Rehabilitation (n=15), 3. Neurotrauma/Neurology/Neurosurgery (n=11), 4. General Sports Medicine (n=11), 5. Neuropsychology (n=7). The analysis identified four organizational clusters with hub institutions: 1. University of North Carolina (n=19), 2. University of Toronto (n=19), 3. University of Michigan (n=11), 4. University of Pittsburgh (n=10). Network analysis revealed 8 clusters for SRC keywords, each with a central topic area: 1. Epidemiology (n=14), 2. Rehabilitation (n=12), 3. Biomechanics (n=11), 4. Imaging (n=10), 5. Assessment (n=9), 6. Mental health/Chronic Traumatic Encephalopathy (n=9), 7. Neurocognition (n=8), 8. Symptoms/impairments (n=5). CONCLUSIONS: The findings suggest that during the past decade SRC research has: 1) been published primarily in sports medicine, pediatric, and neuro-focused journals, 2) involved a select group of researchers from several key institutions, and 3) focused on new topic areas including treatment/rehabilitation and mental health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.049 | 0.067 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".